Source code for optuna.visualization._param_importances

from collections import OrderedDict
from typing import Callable
from typing import List
from typing import Optional

import optuna
from optuna.distributions import BaseDistribution
from optuna.importance._base import BaseImportanceEvaluator
from optuna.logging import get_logger
from import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState
from optuna.visualization._plotly_imports import _imports
from optuna.visualization._utils import _check_plot_args

if _imports.is_successful():
    import plotly

    from optuna.visualization._plotly_imports import go

logger = get_logger(__name__)

[docs]def plot_param_importances( study: Study, evaluator: Optional[BaseImportanceEvaluator] = None, params: Optional[List[str]] = None, *, target: Optional[Callable[[FrozenTrial], float]] = None, target_name: str = "Objective Value", ) -> "go.Figure": """Plot hyperparameter importances. Example: The following code snippet shows how to plot hyperparameter importances. .. plotly:: import optuna def objective(trial): x = trial.suggest_int("x", 0, 2) y = trial.suggest_float("y", -1.0, 1.0) z = trial.suggest_float("z", 0.0, 1.5) return x ** 2 + y ** 3 - z ** 4 sampler = optuna.samplers.RandomSampler(seed=10) study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=100) fig = optuna.visualization.plot_param_importances(study) .. seealso:: This function visualizes the results of :func:`optuna.importance.get_param_importances`. Args: study: An optimized study. evaluator: An importance evaluator object that specifies which algorithm to base the importance assessment on. Defaults to :class:`~optuna.importance.FanovaImportanceEvaluator`. params: A list of names of parameters to assess. If :obj:`None`, all parameters that are present in all of the completed trials are assessed. target: A function to specify the value to display. If it is :obj:`None` and ``study`` is being used for single-objective optimization, the objective values are plotted. .. note:: Specify this argument if ``study`` is being used for multi-objective optimization. For example, to get the hyperparameter importance of the first objective, use ``target=lambda t: t.values[0]`` for the target parameter. target_name: Target's name to display on the axis label. Returns: A :class:`plotly.graph_objs.Figure` object. Raises: :exc:`ValueError`: If ``target`` is :obj:`None` and ``study`` is being used for multi-objective optimization. """ _imports.check() _check_plot_args(study, target, target_name) layout = go.Layout( title="Hyperparameter Importances", xaxis={"title": f"Importance for {target_name}"}, yaxis={"title": "Hyperparameter"}, showlegend=False, ) # Importances cannot be evaluated without completed trials. # Return an empty figure for consistency with other visualization functions. trials = [trial for trial in study.trials if trial.state == TrialState.COMPLETE] if len(trials) == 0: logger.warning("Study instance does not contain completed trials.") return go.Figure(data=[], layout=layout) importances = optuna.importance.get_param_importances( study, evaluator=evaluator, params=params, target=target ) importances = OrderedDict(reversed(list(importances.items()))) importance_values = list(importances.values()) param_names = list(importances.keys()) fig = go.Figure( data=[ go.Bar( x=importance_values, y=param_names, text=importance_values, texttemplate="%{text:.2f}", textposition="outside", cliponaxis=False, # Ensure text is not clipped. hovertemplate=[ _make_hovertext(param_name, importance, study) for param_name, importance in importances.items() ], marker_color=plotly.colors.sequential.Blues[-4], orientation="h", ) ], layout=layout, ) return fig
def _get_distribution(param_name: str, study: Study) -> BaseDistribution: for trial in study.trials: if param_name in trial.distributions: return trial.distributions[param_name] assert False def _make_hovertext(param_name: str, importance: float, study: Study) -> str: return "{} ({}): {}<extra></extra>".format( param_name, _get_distribution(param_name, study).__class__.__name__, importance )